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Polynomial spline estimation for generalized varying coefficient partially linear models with a diverging number of components

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  • Lichun Wang
  • Peng Lai
  • Heng Lian

Abstract

Generalized varying coefficient partially linear models are a flexible class of semiparametric models that deal with data with different types of responses. In this paper, we focus on polynomial spline estimator as a computationally easier alternative to the more commonly used local polynomial regression approach, since one can directly take advantage of many existing implementations for generalized linear models. Furthermore, motivated by the high dimensionality characteristics that accompany many modern data sets nowadays, we investigate its asymptotic properties when both the number of nonparametric and the number of parametric components grows with, but is still smaller than, the sample size. Simulations and a real data example are used to illustrate our proposal. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Lichun Wang & Peng Lai & Heng Lian, 2013. "Polynomial spline estimation for generalized varying coefficient partially linear models with a diverging number of components," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 1083-1103, November.
    • Handle: RePEc:spr:metrik:v:76:y:2013:i:8:p:1083-1103
    DOI: 10.1007/s00184-013-0431-2
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